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Found 11 papers, 3 papers with code
End-to-end Feature Selection Approach for Learning Skinny Trees
Skinny Trees lead to superior feature selection than many existing toolkits e. g., in terms of AUC performance for $25\%$ feature budget, Skinny Trees outperforms LightGBM by $10. 2\%$ (up to $37. 7\%$), and Random Forests by $3\%$ (up to $12. 5\%$).
QuantEase: Optimization-based Quantization for Language Models
Particularly noteworthy is our outlier-aware algorithm's capability to achieve near or sub-3-bit quantization of LLMs with an acceptable drop in accuracy, obviating the need for non-uniform quantization or grouping techniques, improving upon methods such as SpQR by up to two times in terms of perplexity.
Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives
To solve the MIP, we propose a custom nonlinear branch-and-bound (BnB) framework that solves node relaxations with tailored first-order methods.
On Statistical Properties of Sharpness-Aware Minimization: Provable Guarantees
As SAM has been numerically successful, recent papers have studied the theoretical aspects of the framework and have shown SAM solutions are indeed flat.
mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization
Modern deep learning models are over-parameterized, where different optima can result in widely varying generalization performance.
Multi-Task Learning for Sparsity Pattern Heterogeneity: A Discrete Optimization Approach
Allowing the regression coefficients of tasks to have different sparsity patterns (i. e., different supports), we propose a modeling framework for MTL that encourages models to share information across tasks, for a given covariate, through separately 1) shrinking the coefficient supports together, and/or 2) shrinking the coefficient values together.
Improved Deep Neural Network Generalization Using m-Sharpness-Aware Minimization
To that end, this paper presents a thorough empirical evaluation of mSAM on various tasks and datasets.
Sparse NMF with Archetypal Regularization: Computational and Robustness Properties
We consider the problem of sparse nonnegative matrix factorization (NMF) using archetypal regularization.
Recovering Quantized Data with Missing Information Using Bilinear Factorization and Augmented Lagrangian Method
In this paper, we propose a novel approach in order to recover a quantized matrix with missing information.
Transduction with Matrix Completion Using Smoothed Rank Function
In this paper, we propose two new algorithms for transduction with Matrix Completion (MC) problem.
OBTAIN: Real-Time Beat Tracking in Audio Signals
In this paper, we design a system in order to perform the real-time beat tracking for an audio signal.
